1. Spatial Databases: Building Spatial DB
Spring, 2007
Ki-Joune Li

2. Importance of Database
Application of Spatial Databases
(e.g. GIS)
Garbage-In Garbage-Out
About 70% of GIS Development Cost: DB Cost

3. Comparison with Software Lifecycle
Requirement Analysis
Modeling
Schema Design
DB Environments
Data Collection and Input
Quality Control
Maintenance
DB Life Cycle
Requirement Analysis
Functional Specification
Design
Development Environments
Coding
Test
Maintenance
Software Life Cycle – Waterfall Model

4. Requirement Analysis Analysis of Status Output of Analysis
as it is and
as it shall be.
Output of Analysis
Use-Case Diagram of UML: Workflow Analysis
Data items that have been maintained and to be maintained
Description of each item: Data Dictionary
Relationships and Constraints on items
Required accuracy
Spatial Precision
Temporal Precision
Current State: As it is
As it must be

5. Data Dictionary Definitions and Representation of Data Items such as
Precise definition of data elements
Integrity constraints or Constrains
Stored procedures and trigger rules
Specification of
Producer and
Consumer of data element
Why it is so important?
Common understanding on data items
Consistency of databases
Important input to data modeling

6. Data Modeling Data Modeling 4 steps
Understanding the real world and application
A very small piece of the real world
According to viewpoint
Determined by applications
Drawing what you have understood in formal method
Class Diagram in UML
4 steps
Definition of Entities
Attributes of each Entity
Relationships
Constraints

7. -- can optionally be described here.
Class Diagram: Basic
MyClassName
+SomePublicAttribute : SomeType
-SomePrivateAttribute : SomeType
#SomeProtectedAttribute : SomeType
+ClassMethodOne()
+ClassMethodTwo()
Responsibilities
-- can optionally be described here.
Multiplicity
Customer 1
Simple
Aggregation
Class
Abstract
Class
Rental Invoice
Rental Item
{abstract}
1..* 1
0..1
Composition
(Dependency)
Simple
Association
Generalization
Checkout Screen
DVD Movie
VHS Movie
Video Game

8. Definition of Entities
Extract nouns from
Problem statement
Use-Case Diagram
Delete unnecessary entities
Duplication
Attributes rather than entity
ex. Loan amount
Definition of Features
Geographic Entity
Granularity
MyClassName

9. Definition of Features
Meaningful Object of GIS in real world
Must have a geometry
Point, Line, Polygon, etc..
How to define the Granularity of Features
Example
How to define “a” coastal line?
The highway from Pusan to Seoul is a long feature ?
How to separate this long road?

10. Definition of Attributes
Attributes of Feature
Geometric type: Spatial Attribute
Non-Spatial Attributes
Geometric Type
Different Levels of Detail (LOD)
Building
Polygon in 1/1,000 scale
Point in 1/1,000,000 scale
Road
Polyline in 1/1,000,000 scale
MyClassName
+SomePublicAttribute : SomeType
-SomePrivateAttribute : SomeType
#SomeProtectedAttribute : SomeType
+GeometricAttribute

11. Relationship Relationship Non-Spatial Relationship
Spatial Relationship: Topology

12. Constraints Example Implementation
No building on road surface
More than 50 meters between two poles
Implementation
Internal Functions for checking constraints (or constructor)
Spatial OCL (Object Constraint Language)
More detail and complete constraint Better quality of DB

13. Quality Control for Data Modeling
For the quality control,
A Simulation with a pre-defined test scenario

14. Schema Design Automatic Conversion from Data Modeling to Schema
Check Points: Performance Issues
Materialization
Index
Geographic Distribution of DB: Clustering
Based on Workload Analysis
Distribution of operations
Distribution of values

15. Materialization
In SQL, view is a virtual table derived from a Select statement
Eample
CREATE VIEW ExcellentStudents AS SELECT Name, Department, Score FROM Students WHERE Score > 4.0
SELCT Name FROM ExcellentStudents Where Department=‘CS’
ExcellentStudents
Materialization
Invoke

16. Materialize or Not ? Materialization
Duplication
Not 3NF (BCNF)
Cause an inconsistency between the original and derived tables
Update: Overhead due to update propagation
Extra Space Requirements
Should be determined depending on the WORKLOAD
Frequency of updates
Cost for update propagation
Especially when materialized view is geographically distributed

17. Spatial Index Index: Accelerate Search Spatial Index
Spatial predicates: contain, overlapping, k-NN
Much improves the query processing performance
Has a performance overhead for insertion/deletion
2nd Phase
Search Block Number
Search Condition
{ Block# }
Database on Disk
1st Phase

18. Clustering: Placement of records
Vertical Fragmentation vs. Horizontal Fragmentation
Vertical Fragmentation: Decomposition of table
Horizontal Fragmentation: Placement of objects
Consideration on Workload
Vertical Fragmentation
Horizontal Fragmentation

19. Clustering
Clustering: Grouping objects so as to maximize Prob(a C, bC), when OK=a and OK+1=b for any two objects a and b of the same group C.
Spatial Clustering
Basic Assumption: If dist(a,b) < dist(a,c), Prob(OK=a, OK+1=b) > Prob(OK=a, OK+1=c)
Two consecutive accesses a b c

20. Spatial Clustering Methods
k-Means
CLARANS in IEEE TKDE 2002, 14(5)
BIRCH in proc. VLDB 1996
DBSCAN in proc. KDD 1996
SMTIN in proc. ACM-GIS 1997